In view of the degradation of traditional convolution neural network with the deepening of depth and the large amount of calculation, a remote sensing image scene classification method based on improved residual neural network is proposed.This method takes the residual network ResNet50 as the main framework, and introduces deep separable convolution and grouping convolution into the residual structure, which reduces the amount of network parameters and calculation, speeds up the convergence of the model and improves the classification accuracy. In addition, the multi-scale SE block module is embedded in the network to recalibrate the channel features and extract more im-portant feature information, which further improves the classification performance of the network. The classification accuracy of AID and UCMerced_Land Use is 91.92% and 93.52% respectively, which is 3.38% and 10.24% higher than that of conventional residual network, which proves the feasibility and effectiveness of the proposed method in remote sensing image scene classification task.